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Systems Level Analysis of Systemic Sclerosis Shows aNetwork of Immune and Profibrotic Pathways Connectedwith Genetic PolymorphismsJ. Matthew Mahoney1¤*, Jaclyn Taroni1, Viktor Martyanov1, Tammara A. Wood1, Casey S. Greene1,

Patricia A. Pioli2, Monique E. Hinchcliff3, Michael L. Whitfield1*

1 Department of Genetics, Geisel School of Medicine at Dartmouth, Hannover, New Hampshire, United States of America, 2 Department of Obstetrics and Gynecology,

Geisel School of Medicine at Dartmouth, Hannover, New Hampshire, United States of America, 3 Department of Medicine, Northwestern University Feinberg School of

Medicine, Chicago, Illinois, United States of America

Abstract

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis ofSSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative,normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsiessuggests that a patient’s subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many geneticrisk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with theintrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-geneinteraction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expressionmodules common to three independent datasets from three different clinical centers, we developed a consensus clusteringprocedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis ofthese genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecularfeatures across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. Thenetwork is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages,adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connectionsbetween the inflammatory- and fibroproliferative-specific genes. The network also shows connections between thesesubset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, andseveral human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SScsubsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Citation: Mahoney JM, Taroni J, Martyanov V, Wood TA, Greene CS, et al. (2015) Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune andProfibrotic Pathways Connected with Genetic Polymorphisms. PLoS Comput Biol 11(1): e1004005. doi:10.1371/journal.pcbi.1004005

Editor: Shervin Assassi, University of Texas Health Science Center at Houston, United States of America

Received February 13, 2014; Accepted October 27, 2014; Published January 8, 2015

Copyright: � 2015 Mahoney et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the Scleroderma Research Foundation (MLW, MEH; http://www.srfcure.org), P50AR060780 (MLW), P30AR061271 (MLW),and the Scleroderma Foundation Linda Lee Wells Research Grant (PAP; http://www.scleroderma.org/). JMM was supported by Award Number R25 CA134286-01from the National Cancer Institute (NCI). JT was supported by Award Number T32GM008704 from the National Institute of General Medical Sciences (NIGMS). Thecontent is solely the responsibility of the authors and does not necessarily represent the official views of the NIGMS, NIAMS, NCI or the NIH. The funders had norole in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: I have read the journal’s policy and have the following conflicts: MLW and MEH have filed patents for gene expression biomarkers in SSc.MLW is the Scientific Founder of Celdara Medical LLC.

* Email: [email protected] (JMM); [email protected] (MLW)

¤ Current address: Department of Neurological Sciences, College of Medicine, University of Vermont, Burlington, Vermont, United States of America

Introduction

Genome-scale gene expression profiling of systemic sclerosis

(SSc) skin has identified distinct intrinsic molecular subsets

(inflammatory, fibroproliferative, and normal-like) within the

subset of patients diagnosed with diffuse cutaneous SSc (dSSc)

based upon the extent of skin involvement. These subsets are

identified by an intrinsic gene analysis [1] that shifts the focus to

differences between patients rather than patient biopsies. The

inflammatory subset is characterized by increased expression of

genes associated with inflammation and extracellular matrix

(ECM) deposition, while the fibroproliferative subset is character-

ized by increased expression of genes associated with cell

proliferation [1,2]. Biopsies from patients in the normal-like subset

show gene expression most similar to healthy control skin biopsies.

The presence of three distinct molecular SSc subsets within

patients diagnosed with dSSc underscores the molecular hetero-

geneity of SSc. However, it is unclear whether the subsets

represent distinct diseases with different etiologies or whether they

represent disease progression. To address this question, we

identified the conserved molecular pathways characteristic of each

subset that are reproducible between different datasets from

multiple patient cohorts, and examined the connectivity of these

genes and SSc-associated polymorphic genes in a predicted

functional network.

SSc is a rare disease without validated disease progression

markers and no known cure. SSc affects between 49,000–276,000

Americans; one in three patients dies within 10 years of diagnosis

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[3]. The rarity of SSc makes this disease an excellent test case for a

genomic meta-analysis to understand disease mechanism. Using

this approach, we have begun to understand the molecular and

clinical complexity of SSc. Our findings may assist in generating

patient-specific therapies [4] and delivering real-time quantitative

feedback regarding therapeutic response during clinical trials

[4,5].

High-throughput gene expression data has demonstrated that

genes that function together are almost always co-expressed and

thus highly correlated with each other [6,7]. Indeed, the expressed

genes in a biopsy form a co-expression network, where genes serve

as nodes and correlations as links between genes. (The language of

networks is technical and beyond the scope of this paper. The

supporting information (S1 Text) contains a glossary of keywords

that are used in a technical sense in the main body of the paper.)

This observation forms part of the basis for ‘‘network medicine’’

[8,9]. The co-expression network contains groups of highly

correlated genes that represent the biological processes at work

in the tissue [6,10]. These groups of genes can be found using a

variety of procedures for co-expression clustering (e.g. [6,10]). All

of these procedures group highly correlated genes together, i.e.

they partition the genome into non-overlapping groups of genes

with similar expression patterns sometimes called modules. The

output of co-expression clustering is a data-driven partition of the

expressed genes in the genome. As a result of technical differences

in data acquisition protocols as well as true biological variation

(patient heterogeneity, treatment), the exact modules identified in

one SSc dataset differ somewhat from those identified in another

dataset, although the same pathways, biological processes, and

many core genes are found in each dataset. We developed a tool to

compare gene co-expression modules derived from multiple

disparate datasets to identify the modules that are reproducibly

expressed in each dataset. We then derived gene sets from the

overlaps between conserved modules across datasets. These

‘‘consensus clusters’’ are the gene clusters that are conserved

across all datasets.

A naive approach to solving this problem is to simply intersect

the ‘‘intrinsic gene lists’’ derived for each of the cohorts [1,4,11].

The methodological issue with this approach is that these lists are

derived under a large multiple hypothesis testing burden, and

although the same biological processes and some genes are found

reproducibly, the gene sets do not exactly recapitulate across data

sets [11]. This simple intersection approach would be much too

conservative and consequently exclude many biologically impor-

tant genes. Our alternative approach is to consider ‘‘modules

first’’. In brief, our goal is to identify the modules that are

conserved across datasets first and then extract the consensus

genes as those that are consistently assigned to those modules. This

transfers the multiple testing burden onto the much smaller list of

modules and allows genes to be included in the consensus even if

they do not achieve extremely high statistical significance in all

datasets simultaneously.

We developed this idea into a novel data mining procedure

called Mutual Information Consensus Clustering (MICC) to

identify conserved gene expression modules across multiple gene

expression datasets. Consensus clustering is a set of techniques

from computer science and bioinformatics that refers to strategies

for extracting robust clusters from an ensemble of partitions.

Typically this is done using a large ensemble of partitions. Early

work focused on weak clustering algorithms and consensus

clustering was used to ‘‘boost’’ the weak partitions into an

aggregate, consensus partition [12]. In bioinformatics, consensus

clustering algorithms have been developed to aggregate ensembles

of partitions that are derived from data resampling [13]. These

techniques have in common that they do not ‘‘trust’’ a particular

partition from one of their clustering algorithms. Here, we use a

strong clustering algorithm called weighted gene co-expression

network analysis (WGCNA). Our ensemble of partitions is the

collection that we obtain from having multiple, clustered datasets

from independent cohorts. While WGCNA extracts meaningful

signals in each data set, the potentially interesting modules in one

dataset are not precisely replicated in all others.

Mutual information [14] provides a rigorous criterion by which

modules from different datasets can be said to have significant

overlap (i.e. are conserved) and allows one to identify when the

available information between two partitions is exhausted. Mutual

information is a sum of positive and negative contributions from

each pair of modules across datasets, and MICC automatically

disregards all overlaps that do not contribute positively to the total

mutual information, thus giving an objective measure of conserved

gene expression that is both comprehensive and parsimonious. As

such, MICC is a metaclustering procedure that ‘‘clusters the

clusters’’ [12], but does not produce a complete partition. Instead,

it generates only a partition of the subset of the genome that has

strongly conserved gene co-expression.

Using previously published gene expression data from skin

biopsies from patients with SSc recruited at three independent

academic centers [1,4,11] and new samples analyzed as part of this

study (Table 1), we identified the consensus clusters that were

present in all datasets. Due to the unbiased nature of high-

throughput screening, these datasets contain information about

SSc-specific biology as well as the general biology of skin. We

showed that MICC yields consensus clusters that are biologically

specific. At the level of the whole transcriptome, we demonstrated

that the consensus clusters are enriched for hubs defined by co-

expression network analysis. We then filtered the consensus

clusters down to those that were intrinsic subset-specific. The

existence of the intrinsic subsets is a robust observation in each of

these studies and the consensus clusters associated with them

provide a rigorous picture of the core gene set underlying the

subsets. The comprehensive and concise annotation of the

conserved differential gene expression that we developed suggests

Author Summary

Systemic sclerosis (SSc) is a rare autoimmune diseasecharacterized by skin thickening (fibrosis) and progressiveorgan failure. Previous studies of SSc skin biopsies haveidentified molecular subsets of SSc based upon geneexpression termed the inflammatory, fibroproliferative,normal-like, and limited intrinsic subsets. These geneexpression signatures are large and although the biolog-ical processes are conserved, the exact list of genes canvary across datasets due to random variation, as well asminor differences in the composition of the study cohorts(e.g. early vs. late disease). We developed a computationaltool to identify the consensus genes underlying thesubsets across heterogeneous data and characterized thebiological role of the consensus genes in SSc in order toobtain a systems level perspective of the SSc subsets. Ouranalysis reveals a complex network of genes connectingtwo of the major SSc intrinsic subsets, inflammatory andfibroproliferative. Many genetic loci associated with SScrisk show connections with the consensus genes of theintrinsic subsets, indicating that differential expression ofgenes defining the subsets may be related to genetic riskfor SSc, thus for the first time placing the genetic riskfactors in the context of, and showing putative relation-ships with, the intrinsic gene expression subsets.

Systems Level Analysis of Systemic Sclerosis


that the intrinsic subsets represent pathophysiological states of one

disease. Our major findings include the following: 1. We show that

the subset-specific consensus clusters are part of a gene-gene

network and for the first time to our knowledge, demonstrate

putative connections between the intrinsic gene expression subsets

of SSc and SSc-associated genetic polymorphisms identified by

candidate and genome-wide association studies (GWAS). 2. We

provide additional unbiased data to support the hypothesis that

immune system activation is an early event and plays a central role

in SSc pathogenesis as SSc risk alleles are linked to the immune

system nodes of our network. 3. The consensus gene-gene network

provides insights into genes that may be central to the major

disease processes and identifies genes and pathways that may

connect these major groups of genes. 4. We show a link between

the inflammatory and fibroproliferative patient groups through a

shared TGFb/ECM subnetwork, suggesting a theoretical path by

which these gene expression subsets may be linked. Collectively,

these findings demonstrate that MICC is a powerful tool that

identifies the reproducible signals in gene expression data across

multiple datasets and shows how they may relate to the genetic

polymorphisms associated with SSc.

Results

We analyzed a compendium of three whole transcriptome

datasets from SSc skin biopsies (Milano et al. [1], Pendergrass et al.

[11], and an expanded version of Hinchcliff et al. [4]; see

Materials and Methods). These datasets consist of 70 patients with

dSSc, 10 patients with limited SSc (lSSc), 4 morphea samples, and

26 healthy controls (Table 1). Our aim was a comprehensive

picture of the gene expression abnormalities in SSc skin and we

integrated several publicly available tools with a novel consensus

clustering procedure. As demonstrated in Fig. 1, our analysis

began with gene coexpression clustering (Fig. 1A), followed by a

novel post-processing step called Mutual Information Consensus

Clustering (MICC) that identified conserved gene expression

modules across the three cohorts (Fig. 1B). The outputs from

MICC were consensus clusters, i.e. modules that were conserved

across datasets, which were the objects of further study, including

ontology annotation and functional interaction analysis (Fig. 1C).

To understand the molecular processes at work in SSc skin

biopsies, we constructed data-driven partitions of the expressed

genes across multiple SSc skin gene expression datasets using

weighted gene co-expression network analysis (WGCNA) [10]

(Figs. 1A, 2). Each co-expression cluster, or module, in the

partition corresponds to a collection of correlated molecular

processes present in the SSc tissue at the time of biopsy. To

compare these modules across SSc datasets, we used mutual

information to detect when a module from one dataset is present

in another dataset. The partitions of the genome-wide expression

data vary from one dataset to the next due to clinical heterogeneity

and treatment effects, as well as technical variation in RNA

processing protocols. All samples were analyzed on Agilent DNA

microarrays with the same DNA probes in the same laboratory,

providing consistency of the gene expression data and genes

analyzed.

To identify genes with conserved expression across multiple

datasets, we developed a procedure called Mutual Information

Consensus Clustering (MICC) that detects significant conservation

of a piece of a module and groups these conserved modules into

collections called communities, which are sets of modules with

considerable mutual overlap between datasets. Each community is

associated with a gene set; namely all genes that are annotated to a

module in that community for each dataset. We call these gene sets

consensus clusters. The basis of MICC is the concept of mutual

information from information theory [14]. Specifically, we use

mutual information of partitions (MIP), which is an information

measure specific for partitions. MIP quantifies the amount of

information one partition has about another; i.e. it measures the

correlation of cluster labels across datasets. MICC identifies

consensus clusters using MIP to build a module similarity network

of significant module overlaps, which we call the information

graph (Figs. 1B, 3A). Then MICC algorithmically identifies

communities in that network (Fig. 1B; Fig. 3A, colored nodes).

These communities are collections of modules that have substan-

tial overlap among each other, and they represent nearly all of the

mutual information between the genomic partitions. In this way,

MICC extracts almost all of the available information present in

the separate clusterings of individual datasets and reports the

clusters that are conserved across the three cohorts (see Materials

and Methods for a detailed description).

WGCNA identifies that large numbers of genes in thegenome are deregulated in SSc skin

Weighted gene coexpression network analysis (WGCNA) [10] is

a gene co-expression clustering procedure that automatically

detects the number of modules in a dataset and removes outlier

genes. WGCNA performed on a single SSc skin dataset (Milano et

al. [1], S1 Data file), demonstrates the complexity of comparative

studies across multiple datasets (Fig. 2). The molecular subsets

termed ‘SSc intrinsic subsets’ were first identified by Milano et al.

[1]. The Milano et al. dataset is the best characterized dataset

showing the SSc intrinsic subsets that has been analyzed to date.

These data were clustered into modules by WGCNA, and the

resulting modules were summarized by their first principal

component or module eigengene (Fig. 2). Module eigengenes are

a one-dimensional summary of the gene expression within a

module that captures the bulk of the variance within that module.

To identify those modules that were intrinsic subset-specific, we

performed Kruskal-Wallis tests on the module eigengenes with

groups defined according to intrinsic subsets. Of the 54 total

modules, 23 had a significant p-value for association with the

intrinsic subsets (all p,0.05 after Bonferroni correction; Fig. 2

shows six representative examples). These 23 modules comprise

approximately 40% of the expressed genes in the genome

(Table 2), demonstrating that the intrinsic subsets found in SSc

skin are defined by deregulation of a very large fraction of the

expressed genes in any given cell. Gene expression for six

significant modules along with their corresponding module

eigengenes demonstrates clear association with the intrinsic subsets

(Fig. 2). These modules are enriched for broad functional

categories previously associated with SSc, including chemokine

signaling, NFkB signaling, RAS-RAC signaling in the inflamma-

tory subset, and cell cycle processes in the fibroproliferative subset

[1,4,11].

To identify the core set of genes reproducibly found in each SSc

intrinsic subset, we performed WGCNA on two additional SSc

skin gene expression datasets: Pendergrass et al. [11] and an

expanded version of Hinchcliff et al. [4] (Data files S2–S3). The

Hinchcliff data are from an ongoing clinical trial of mycopheno-

late mofetil (MMF). Preliminary data have been published [4], but

we also analyzed data for an additional 82 unpublished samples

from that trial (the full data available from NCBI GEO at

GSE59787). A summary of the three cohorts, including the

expanded Hinchcliff cohort, is available in Table 1. Each of the

three datasets had approximately 60 modules. The module

eigengenes were tested for association with the intrinsic subsets,

and it was found that, within a dataset, between 18% and 67% of



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the modules were subset-specific (Table 2). This shows that for

each dataset a substantial fraction of modules was associated with

the intrinsic subsets and, that 4,004–10,373 genes out of the

approximately 19,500 in the human genome were in differentially

regulated modules associated with the intrinsic subsets (Tables 2

and 3).

The gene co-expression modules represent biological processes

that are active in skin and some are reflective of disease

pathogenesis. To determine which processes were conserved

across all three datasets, we constructed the information graph

for the three separate WGCNA partitions of the genome (Fig. 3A).

The information graph is a network where a node in the network

is a module from one dataset, and a link between modules

indicates that the overlap between those modules is significantly

larger than would be expected at random. In other words, an edge

represents conservation of a significant part of a module across two

datasets (see Materials and Methods for a detailed discussion of

module overlap scores). Triangles in the information graph

correspond to a significant three-way overlap of modules or,

equivalently, a module conserved across all three datasets. We

enumerated all triangles in the information graph to identify all

such conserved modules. There were 157 triangles and approx-

imately 2000 genes in their corresponding triple overlaps. Most

(129 out of 178) of the modules across all SSc datasets are present

in at least one triangle, i.e. most co-expression modules had a

significant portion co-expressed in the other datasets (Table 2,

bottom row). This indicates that the WGCNA-derived modules

are reproducible features of SSc gene expression. Nine of the

triangles had all three nodes (modules) significantly associated with

the subsets (five inflammatory, four fibroproliferative; see below

and Fig. 3).

The consensus genes are hubs in the gene-gene co-expression

networks. To see this, we noted that module eigengenes represent

hubs in the gene-gene correlation network [15]. A module

eigengene does not correspond to an actual gene, but rather

represents a theoretical gene that is most central in the module.

Fig. 1. Schematic of the analysis pipeline for integrative analysis of multiple SSc skin datasets. (A) Each microarray dataset (Milano et al.,Pendergrass et al., and Hinchcliff et al.) was independently clustered by WGCNA into gene coexpression modules (colored circles). Each module is aset of genes that was highly correlated within a dataset. (B) Modules were compared across datasets using a novel procedure (MICC) to determinewhich were approximately conserved across all three datasets. The network in (B) is called the information graph and encodes the nontrivial overlapsof modules across datasets. Triangles in this network correspond to approximately conserved modules across all three datasets. Communities in thisnetwork (dotted ovals) represent collections of modules that are conserved together and thus have similar biological function. Note thatcommunities in the network can overlap (e.g. module P1 in the schematic belongs to two communities). (C) Genes derived from the modulecommunities are called consensus genes and were used for downstream bioinformatics analyses including gene ontology enrichment analysis usingthe g:Profiler tool, testing for intrinsic subset-specificity, and functional interaction network analysis using the IMP functional network. Each of thesedownstream analyses is independent and complementary.doi:10.1371/journal.pcbi.1004005.g001



Therefore, genes that are highly correlated to their module

eigengene are more central within their module. We calculated the

correlation of each gene to its corresponding module eigengene

(S1 Fig.). The density of these gene-eigengene correlations is

shown for all genes in the genome (blue curve) and for only the

consensus genes (red curve). The consensus genes are significantly

more correlated with their module eigengene than randomly

selected genes are with their module eigengene, indicating that the

consensus genes are significantly enriched for hub genes in their

(dataset-specific) co-expression network. This is a useful positive

control for the MICC method because it shows that the consensus

genes are enriched for ‘‘hubness’’ in the SSc co-expression network

and thus MICC finds genes that have salient network features.

The information graph reveals conserved, subset-specificmolecular modules

While most modules are partially conserved between the three

datasets and many of them are intrinsic subset-specific, not all

intrinsic subset-specific modules are conserved across all datasets

(S4 Data file). To find the conserved, intrinsic subset-specific

modules, we noted that the information graph has groups of

triangles with considerable mutual edge-sharing (Fig. 3A). Many

of the triangles in the information graph overlap and form

communities of triangles (Fig. 3A, S2 Fig.). This was intriguing

because it opened up a broader interpretation of ‘‘consensus

cluster’’. If the information graph had been a disconnected

collection of single triangles, this would have implied that there

was a one-to-one mapping between the modules from different

datasets. Instead, a single module from one dataset gets broken

into pieces in the other datasets. The community structure of the

information graph indicates what we have known from many prior

microarray studies, namely that specific groups of genes are

commonly expressed together and that the aggregate set of genes

underlying these multiple co-expression clusters constitutes the

truly conserved processes in SSc [1,6,16].

We detected communities in the information graph using a

variant of clique percolation [17], a network community detection

procedure that, in this case, explicitly identifies communities of

triangles (S1 Text). Clique percolation identified 26 communities,

13 of which were single, isolated triangles, while the rest were

groups of more than one triangle (Fig. 3A, S2 Fig.).

To derive a gene set associated with a community in the

information graph, we took all modules within the community,

computed their union within datasets, and computed their

intersection across datasets (S3 Fig.). In this way, we captured all

genes whose co-expression was conserved across the three datasets.

(A mathematical description of this procedure is presented in the

Fig. 2. Gene expression modules associated with the intrinsic subsets of SSc. We identified 54 major sets of genes (modules) using WGCNAthat define the spectrum of gene expression in SSc skin using Milano as a test case. The top 6 most significant modules are shown and each shows astatistically significant association with the intrinsic subsets (including the limited subset). Module assignment for each gene is unique. The genesthat compose the subset-specific modules represent more than 40% of the protein-coding genes in the human genome. Therefore, the intrinsicsubsets seem to be determined by a large fraction of the encoded genes. The module eigengene of each module is shown in a stem-plot below eachheatmap with intrinsic subsets indicated by color above the heatmap. Proliferative, red; inflammatory, purple; limited, yellow; normal-like, green. (A)Inflammatory modules (p,1029 and p,1027; Kruskal-Wallis non-parametric ANOVA corrected for multiple testing), (B) Limited Module (p,0.006),(C) Fibroproliferative modules (p,1027; p,1028), (D) Fibroproliferative and Limited expression module (p,1029). Enriched molecular processes areindicated for each subset to the right of each heat map.doi:10.1371/journal.pcbi.1004005.g002



Materials and Methods.) We termed these community-derived

gene sets consensus clusters (CCs). Using g:Profiler [18], we found

that the consensus clusters are enriched for many biological

processes (summary in Table 3; raw data in S5 Data file) present

in both healthy and SSc biopsies. For example, CCs 1, 4, 5, 7, 8,

and 11 are enriched for basic metabolic and cellular processes,

while CC 12 showed enrichment for keratinocyte-specific

processes (Table 3; Fig. 3A, cyan). These consensus clusters show

Fig. 3. Information graph and consensus clusters for the MPH cohorts. (A) The information graph of the MPH cohorts is highly modular (cf.S2 Fig.), indicating approximate conservation of gene expression modules across datasets. The information graph is tripartite by construction, so atriangle in the graph necessarily connects modules across all three datasets. The triangles form communities of mutual edge sharing. Colored nodesand edges highlight four of these communities. The purple community contains modules that are up-regulated in the inflammatory subset (cf. panelB). The red community contains modules that are up-regulated in the fibroproliferative subset (cf. panel B). The cyan community contains modulesthat are enriched for keratinocyte-specific processes. The orange community contains modules that are enriched for fatty acid metabolism genes. Theremaining communities (22 in all and not colored to avoid cluttering the display) are enriched primarily for housekeeping processes and are neitherskin- nor disease-specific (see Table 3). (B) Modules from the communities were tested for their enrichment in the subsets. Each row corresponds to atriangle in the information graph and each column corresponds to a dataset. The black lines separate communities, e.g. all of the rows in the blockmarked ‘‘1’’ correspond triangles in community 1. The cells are colored according to whether the module was significantly differentially expressed in asubset with dark colors representing up-regulation and light colors representing down-regulation (Bonferroni-corrected Wilcoxon rank sum p-valuep,0.05). We assessed statistical significance of modules within each dataset for each of the three diffuse SSc intrinsic subsets, as well as all SSc vs.healthy controls (Purple- Inflammatory, Red- Proliferation, Green- Normal-like, Blue- All SSc). Note the inflammatory up community (*) and thefibroproliferative up community (**). Note also that community 2 is significantly highly expressed in the inflammatory subset and lowly expressed inthe proliferative subset in Milano only. Likewise, community 9 appears to be expressed at low levels in the inflammatory subset in Milano, but noneof the other data sets.doi:10.1371/journal.pcbi.1004005.g003

Table 2. Statistics of module conservation.

Dataset Milano Pendergrass Hinchcliff

Number of subset-specific modules (Number of modules total) 17 (54) 13 (66) 31 (58)

Portion subset specific 31% 18% 53%

Number of genes in subset-specific modules 10,373 4,004 8,517

Number of conserved modules 32 50 47

Portion conserved 59% 76% 81%

The number of modules identified by WGCNA varied across datasets. However, a large majority of modules from each dataset were at least partially conserved acrossthe three cohorts, meaning that they were present in at least one triangle of the information graph (Fig. 3A). A smaller fraction of the modules were subset-specificwithin their dataset. The Hinchcliff dataset had the largest fraction of subset-specific modules, which may be due to subject enrollment in the cohort rather than to SScbiology. The Milano dataset had the largest number of genes present in subset-specific modules. This shows that the subsets are associated with a large number ofgenes (up to 40% of the genome in Milano et al. assuming an upper bound of 25,000 human genes).doi:10.1371/journal.pcbi.1004005.t002



that MICC extracts biologically coherent sets of genes that are

known to be active in skin as consensus clusters. This provides an

additional positive control for the MICC method.

More importantly, CC3 and CC9 showed enrichment for

processes implicated in SSc (Table 3; S5 Data file). CC3 was

enriched for response to interferons, B cell receptor signaling,

monocyte chemotaxis, and TGFb and PDGF signaling, as well as

ECM remodeling processes. CC9 showed enrichment for cell cycle

and cell proliferation processes, as well as integrin interactions with

fibrin. Note that CC3 and CC9 both show enrichment for distinct

ECM-related molecular processes. These data are consistent with

the analysis of experimentally derived pathway signatures [19].

The consensus clusters CC3 and CC9 map to the major

intrinsic subsets previously described [1]. We tested every module

for association with the intrinsic subsets (see Materials and

Methods) and we constructed a ‘‘heatmap’’ of the triangles in

the information graph by dataset (Fig. 3B). The rows were ordered

by community membership and the columns were ordered by

dataset. We concatenated each of these plots so all subsets,

datasets, and consensus clusters can be viewed simultaneously.

Only consensus clusters 3 and 9 were enriched for SSc intrinsic

subset specificity. Consensus cluster 3 contained modules that are

almost all significantly expressed at high levels in the inflammatory

group of patients (Fig. 3A, purple nodes; Fig. 3B). Consensus

cluster 9 contains modules that are almost all significantly

expressed at high levels in the fibroproliferative group (Fig. 3A,

red nodes; Fig. 3B). We also included tests for all SSc biopsies

versus healthy controls to determine if there were any consensus

clusters that were generally conserved across all SSc biopsies.

There were no consensus clusters that were enriched for all SSc

versus healthy controls, which illustrates quantitatively SSc

heterogeneity. Furthermore, there were no consensus clusters that

were consistently expressed at low levels in any of the subsets.

Some consensus clusters are enriched for a subset in some of the

datasets, but are not replicated across all datasets (Fig. 3B). For

example, CC2 is expressed at high levels in the inflammatory

subset and low levels in the proliferative subset in Milano, but

neither of the other datasets. Inflammatory-specific CC3 is

expressed at low levels in the proliferation subset in Milano and

in the normal-like subset in Pendergrass and Hinchcliff, and is

expressed at high levels in all SSc versus healthy controls in

Pendergrass and Hinchcliff only. Similarly, CC9, which is

proliferative-specific, is expressed at low levels in the inflammatory

subset in Milano only. These observations demonstrate that genes

with increased expression should be the focus in SSc.

Conserved inflammatory and fibrosis genes form anetwork with putative SSc risk alleles

The biology of CC3 and CC9 show the processes common to

the intrinsic subsets that have been observed across multiple gene

Table 3. Molecular processes enriched in the 13 largest consensus clusters.

Consensus Cluster Enriched Molecular Processes # Consensus genes

1 Tubulin processing 1144

2 Insulin signaling 1194

3 TGFb signaling 312

PDGF signaling

Collagen fibril organization

B cell receptor signaling

Monocyte chemotaxis

Response to interferons

Patterning of blood vessels

4 RNA processing and transport 873

Ribosome biogenesis

5 Tubulin processing 485

6 ARF activity 225

7 Mitochondria 73

8 Mitochondria 80

9 Fibrin/fibrinogen 274

Mitotic spindle

G2/M transition

Integrin interactions with fibrin

10 Alcohol metabolism 80

11 Fatty acid metabolism 98

12 DAP12 (TYROBP) signaling 63

Keratinocyte processes

13 Phosphatidic acid 11

An analysis with g:Profiler resulted in many biological processes enriched in the each of consensus clusters. This table contains a condensed list of the significantpathways (p,0.05, corrected for multiple testing by default in g:Profiler), retaining those that are specific for skin or other housekeeping biology. Note particularlyconsensus clusters 3 and 9, which are enriched for the inflammatory and fibroproliferative subsets respectively. Consensus clusters 3, 9 11, and 12 are highlighted inFig. 3A by the colored communities in the information graph.doi:10.1371/journal.pcbi.1004005.t003



expression datasets: inflammation, cell interactions with ECM,

and cell proliferation (Table 3). To determine if there was a more

interconnected relationship between these conserved processes

(such as genes related to specific cell types) than could be gained

from an ontological annotation analysis like g:Profiler, we used

CC3 and CC9 as a query gene set for the IMP gene-gene

interaction Bayesian network (IMP) (Fig. 4) [20]. IMP is a gene-

gene interaction network developed using a large compendium of

high-throughput biological data including all publicly available

microarray data that predicts the probability that pairs of genes

have a co-expression interaction. A list of genes is imported into

IMP, and a list of high-probability interactions between the genes

on the imported list and (up to 50 additional genes in) the rest of

the genome is generated. IMP is completely agnostic to SSc-

specific biology and reports predicted interactions that are based

on the preponderance of evidence across all publicly available

gene expression data. As our query, we pooled the two consensus

clusters CC3 and CC9 to discover possible molecular links

between the inflammatory and fibroproliferative intrinsic subsets.

We added polymorphic genes from genome-wide association

studies (GWAS), as well as genes from candidate gene studies that

have been replicated in at least one follow up study (see Materials

and Methods; S6 Data file). In addition, we added four genes that

are putative predictors of Modified Rodnan Skin Score (MRSS), a

widely used clinical measure of skin fibrosis [5].

The output network from IMP was dominated by one large

interconnected network that had five distinct subnetworks (Fig. 4;

S7–S9 Data files). The five molecular subnetworks were each

enriched for a distinct biological process: interferon response, M2

macrophage activation, adaptive immunity, ECM deposition and

remodeling and TGFb signaling, and cell proliferation.

One subnetwork was dominated by interferons and interferon-

inducible genes (Fig. 4, top middle; S9 Data file). The interferon

subnetwork contained genes solely from the inflammatory

consensus cluster (Fig. 4, purple nodes). This subnetwork con-

tained the interferon inducible genes IFI16 and IFI44, the latter

Fig. 4. Molecular network of inflammatory and fibroproliferative consensus genes. The consensus genes for the inflammatory andfibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes arecolored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (IFI44) wasalso an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinctsubnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFb signaling, adaptive immunity, and cell proliferation. Theinterferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECMsubnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genesindicating that the genetic risk in SSc is related to immune abnormalities.doi:10.1371/journal.pcbi.1004005.g004



of which is a putative biomarker of fibrosis [5]. This subnetwork

also contains the polymorphic interferon regulatory factor genes

IRF5, IRF7, and IRF8.

A second subnetwork contained genes characteristic of M2

macrophage activation (Fig. 4, bottom left; S9 Data file). The

genes in this network, which include major histocompatibility

complex (MHC) class II genes with SSc-associated polymor-

phisms, are derived primarily from the inflammatory consensus

cluster, implicating macrophages as mediators of inflammation.

Polarized macrophages can broadly be categorized as ‘‘classically

activated’’ (M1) or ‘‘alternatively activated’’ (M2), although it is

important to recognize that macrophage polarization encompasses

a broad spectrum of activation states. M1 macrophages may be

elicited through stimulation with IFN-c and LPS, are microbicidal,

and promote Th1-mediated immune responses. In contrast, M2

cells, which mediate immune suppression, may be activated by

various stimuli, including IL-4 and/or IL-13, which are elevated in

SSc sera [21,22]. Genes associated with M2 activation, including

CX3CR1 [23], IL10R [24], and HLA-DMB [25], were consis-

tently expressed in this subnetwork, in accordance with previous

studies that found increased M2-polarized macrophages in SSc

skin compared to healthy skin [26]. As M2-polarized cells regulate

vascularization and are a potent source of TGFb, PDGF, and

inflammatory cytokines [27–29], activated M2 macrophages may

play a role in mediating fibrosis and inflammation in SSc.

A third molecular subnetwork contained genes related to

adaptive immunity (Fig. 4, top left; S9 Data file). There are

relationships to both B and T cells in the genes in this subnetwork.

Two chains of the T cell receptor complex are represented: CD3G(gamma chain of the T cell receptor (CD3)) and CD247 (the zeta

chain of the T cell receptor), which contains SSc-associated

polymorphisms. The IL-12 pathway, which mediates Th1 cell

differentiation and activation [30,31], is represented through

IL12RB2. Binding of IL-12 to IL12RB2 on activated T cells

initiates a signal transduction cascade that results in activation of

STAT transcription factors, including STAT4 [32] (also repre-

sented in this subnetwork), which regulate T cell signaling and

immune activation [33]. Aberrant expression of IL12RB2 has

been reported in autoimmune and infectious diseases [34,35],

implicating this gene as an important regulator of inflammation

and immune defense.

B cell receptor activation and signaling are also represented in

this subnetwork. DOCK10 expression is up-regulated in B cells by

pro-inflammatory IL-4 [36], and BANK1 and BLK are B cell

proteins that have polymorphisms associated with SSc. Both LYNand CSK appear in this subnetwork and are directly connected to

each other. The tyrosine kinase LYN, which plays a critical role in

down-regulating B cell activation and mediating self-tolerance

[37,38], is phosphorylated by CSK [39]. Polymorphisms in CSK

have been linked to both SSc and systemic lupus erythematosus

(SLE) and are associated with aberrant B cell signaling [40]. CSK

also associates with Lyp [41], which is the product of the tyrosine

phosphatase PTPN22. The PTPN22 gene also contains an SSc-

associated polymorphism. Mutations in PTPN22 that interfere

with its ability to bind to CSK also interfere with both B and T cell

receptor activation [42,43]. Moreover, mutations in PTPN22have been reported in a variety of other autoimmune diseases,

including SLE, rheumatoid arthritis, and type 1 diabetes [44].

Negative regulators of B and T cell activation such as SOCS2and SOCS3, are included in this network. SOCS3 has been shown

to directly inhibit IL-12-induced STAT4 activation [45]. The co-

occurrence of pro- and anti-inflammatory signals in this subnet-

work is notable and is likely because our data are derived from

whole skin biopsies (see Discussion).

The fourth molecular subnetwork contained TGFb pathway

genes (which have long been implicated in the activation of fibrosis

in SSc [46,47]) and ECM structural proteins (Fig. 4, bottom

middle). This TGFb/ECM subnetwork contained genes from both

the inflammatory and fibroproliferative consensus clusters (Fig. 4,

red and purple nodes; S9 Data file). We also found expression of

genes associated with Notch signaling such as NOTCH4, which

contains SSc-associated polymorphisms, and with the epithelial-

mesenchymal transition (EMT) such as LATS2. Alternatively

activated macrophages are known to produce large quantities of

TGFb in SSc pulmonary fibrosis [29], suggesting that the M2

macrophage subnetwork could drive activation of the TGFb/

ECM subnetwork.

The final molecular subnetwork contained cell cycle/cell

proliferation genes, which were primarily from the fibroprolifera-

tive consensus cluster (Fig. 4, right). The expression of prolifera-

tion genes is commonly observed in cancer [2,7,48] and their

presence in the gene expression data of SSc was a surprising and

unexpected finding [1]. The large and densely interconnected

subnetwork of genes in Fig. 4 (right, red nodes) was composed

almost exclusively of cell cycle-regulated genes including AURKA/B, CCNA2, CCNB1, CHK1, and DHFR [7]. This subnetwork

was conserved and showed increased expression in the fibropro-

liferative subset of patients across all three cohorts, and constituted

the core gene expression signature in that subset of patients (Fig. 4,

red nodes). Therefore, the cell proliferation signature of the

fibroproliferative subset of patients first observed in Milano et al.

[1] is a conserved feature of SSc across three independent cohorts

from three separate clinical centers. This molecular subnetwork

has connections to each of the other four subnetworks (interferon,

M2 macrophages, adaptive immunity, and TGFb/ECM) suggest-

ing that cell proliferation in SSc skin is modulated by the

inflammatory and ECM remodeling processes in skin.

IMP predicts that the genes linked to SSc-associated polymor-

phisms (30/41 total) and the putative MRSS biomarker genes of

Lafyatis and co-workers (4/4) have interactions within this large

component of the molecular network (Fig. 4). Polymorphisms in

IRF5, IRF7, and IRF8 were linked to the interferon subnetwork.

IRF7 is also differentially expressed in the inflammatory subset.

The polymorphisms associated with human leukocyte antigen

(HLA) alleles predominantly have interactions with the M2

macrophage subnetwork of genes. Polymorphisms in and differ-

ential expression of NOTCH4 were linked to the TGFb/ECM

subnetwork. The same was true for the MRSS biomarker genes;

IFI44 was linked to the interferon subnetwork; SIGLEC1 was

linked to the M2 macrophage subnetwork; and both COMP and

THBS1 were linked to the TGFb/ECM subnetwork. These

results suggest that prediction of worsening skin disease requires

sampling genes from each molecular subnetwork.

The molecular network contains SSc-pathology-specifichubs

The molecular network contains genes that are hubs (i.e. highly

connected nodes) of the subnetworks.

Interferon-induced protein 44 (IFI44) is a hub of the interferon

subnetwork. It has conserved high expression across all three of

our cohorts in the inflammatory subset and is one of the most

highly connected genes in the interferon subnetwork (Fig. 5, top

right). IFI44 is predicted to have co-expression interactions with

several other interferon-inducible and interferon-regulating genes,

including IFI16, IRF7, IFITM2, ISG20, GBP1, and TRIM22.

Allograft Inflammatory Factor 1 (AIF-1) is a hub of the M2

macrophage. AIF1 is consistently highly expressed in the

inflammatory subset across all three SSc skin cohorts and is one



of the most highly connected genes in the M2 macrophage

subnetwork (Fig. 5, bottom left). In the molecular network (Fig. 5,

bottom left), AIF1 has many links including: ITGB2, a binding

partner of the monocyte marker ITGAL, and MHC class II genes

HLA-DMB, HLA-DPA1, and HLA-DQB1. In addition, AIF1 has

connections to chemokine receptors CCR1 and CX3CR1, which

are connected to chemokines CX3CL1 (fractalkine) and CCL2(MCP-1).

The tyrosine kinase gene LYN is a hub of the adaptive immunity

subnetwork (Fig. 5, top left). LYN has predicted edges with four

polymorphic genes in this subnetwork: BLK, BANK1, CSK, and

GRB10. LYN also has connections to the polymorphic, bridge

genes PLAUR and LCP2 (see below), and suppressors of cytokine

signaling genes SOCS2 and SOCS3. The conserved finding of high

expression of LYN in the inflammatory subset and its centrality

within the adaptive immune subnetwork suggests that LYN plays a

key role in the adaptive immune component of SSc in skin.

Fibrillin-1 (FBN1) is a hub of the TGFb/ECM subnetwork

(Fig. 5, bottom right). High expression of FBN1 is conserved

across the inflammatory subset of all three cohorts of SSc skin, and

FBN1 is highly connected within the TGFb/ECM subnetwork of

the molecular network (Fig. 5, bottom right). The TGFb/ECM

subnetwork includes genes that primarily show high expression in

the inflammatory subset but also includes genes that are highly

expressed in the fibroproliferative group, thus providing a putative

molecular link between the two groups. FBN1 has predicted

connections to many genes whose increased expression is

conserved, including: pro-fibrotic genes including COL1A2,

COL5A2, and elastin (ELN), CTGF, SPARC, THBS1, THBS4,

COMP, TNC and ECM remodeling and wound response genes

LOX, NNMT, and FBLN5. In addition, FBN1 has connections

with growth factor genes and receptors such as HTRA1 and

NOTCH4; cell adhesion genes CDH11 and LAMA4; as well as

the complement system gene C1S.

The molecular network shows genes that bridgesubnetworks

In addition to containing discrete subnetworks, the molecular

network also shows genes that bridge the subnetworks (Fig. 6).

These genes are of particular interest because they have predicted

connections between multiple, distinct subnetworks. The primary

reason for using CC3 and CC9 simultaneously as queries to IMP

Fig. 5. Hubs in the inflammatory and ECM components of the network. The putative MRSS biomarker gene IFI44 is a hub of the type 1interferon subnetwork. AIF1, which contains SSc-associated polymorphisms and is related to M2 macrophage polarization, is a hub of the M2macrophage network. FBN1, which contains SSc-associated polymorphisms in some populations and is a key component of ECM that regulatesmatrix stiffness, is a hub of the TGFb/ECM network. The tyrosine kinase gene LYN is associated with B cell activation and mediating self-tolerance andis a hub in the adaptive immunity subnetwork.doi:10.1371/journal.pcbi.1004005.g005



was to identify possible molecular connections between the core

molecular processes of the inflammatory and fibroproliferative

intrinsic subsets. The bridge genes live at the interfaces between

the subnetworks that constitute these core molecular processes.

The genes CXCR4 and LCP2 are the major connections

between the adaptive immunity subnetwork and the M2

macrophage subnetwork (Fig. 6). LCP2 (SLP-76), which modu-

lates T cell activation [49], has predicted interactions with AIF1and IL10RA in the M2 macrophage subnetwork and to SOCS2,

SOCS3, STAT4, LYN, and CSK in the adaptive immunity

subnetwork (see edges extending from LCP2 in Fig. 6). The

chemokine CXCR4 has predicted interactions with the cytokines/

chemokines IL10RA, CX3CR1, CCR1, and the polymorphic

CCR6 in the M2 macrophage subnetwork (Fig. 6). CXCR4 has

predicted interactions with SOCS3 and JAK3 in the adaptive

immunity subnetwork.

GRB10 contains an SSc-associated polymorphism and is also

expressed at high levels in the inflammatory subset (see blue

GRB10 node, Fig. 6). GRB10 is part of a complex path from the

adaptive immune subnetwork to the M2 macrophage subnetwork

that includes genes containing pleckstrin homology domains

including PLEKHO1, PLEKHO2, CYTH4 and ADAP2.

The major connection between the M2 macrophage subnet-

work hub AIF1 and the interferon subnetwork hub IFI44 is

through RAC2. RAC2 encodes a member of the Rac family of

signaling molecules and has multiple predicted interactions with

both the interferon subnetwork and the M2 macrophage

subnetwork (Fig. 6). In the interferon subnetwork (Fig. 6, upper

middle), RAC2 connects to CTSC (cathepsin C), IFITM1 and

IFI16, as well as the Rho GTPase related genes ARHGDIB and

RAB31. In the M2 macrophage subnetwork (Fig. 6, lower left),

RAC2 connects to ITGB2, the actin cytoskeleton related proteins

LCP1 and COTL1, and GMFG. COTL1 is also related to

leukotriene biosynthesis through a known interaction with

ALOX5. These diverse interactions suggest that RAC2 is involved

simultaneously in macrophage motility, leukotriene biosynthesis,

and interferon signaling.

The major bridges between the M2 macrophage subnetwork

and the ECM subnetwork are THY1 (CD90) and CD14 (Fig. 6,

lower left). THY1 connects to SIGLEC1, MXRA5 and COL1A2.

THY1 mediates adhesion of leukocytes and monocytes to

endothelial cells and fibroblasts [50], may also have a role in lung

fibrosis (a major complication of SSc); THY1 knockout mice have

increased lung fibrosis [51,52]. CD14 is a cell surface protein

Fig. 6. Bridges between components of the network. Several genes bridge the component subnetworks of the molecular network. PLAUR is agene that contains SSc-associated polymorphisms that forms a bridge between the interferon subnetwork and TGFb/ECM subnetwork. The geneRAC2 is a bridge between the interferon and M2 macrophage subnetworks. The genes LCP2 and CXCR4 are bridges between the M2 macrophagesubnetwork and the adaptive immunity subnetwork. There are also several paths through GRB10 to ADAP2 between the M2 macrophage subnetworkand the adaptive immunity subnetwork. The genes CD14 and THY1 (CD90) are bridges between the M2 macrophage subnetwork and the TGFb/ECMsubnetwork. The genes IRAK1 and PXK are bridges between the TGFb/ECM subnetwork and the cell proliferation subnetwork.doi:10.1371/journal.pcbi.1004005.g006



mainly expressed by macrophages, is inducible by and connected

to AIF1 [53]. It also has connections to the polymorphic genes

TLR2 and HLA-DRA (Fig. 6, lower left).

PLAUR (UPAR) contains a putative SSc-associated polymor-

phism, is a member of the interferon subnetwork, and has

numerous links with the ECM, M2 macrophage, and the adaptive

immunity subnetworks (Fig. 6). PLAUR encodes the plasminogen

activator, urokinase receptor protein and is a pleiotropic gene at

the interface of ECM remodeling, as a component of the

fibrinolysis system, and in both adaptive and innate immune

processes, including monocyte migration [54]. PLAUR is induc-

ible by proinflammatory cytokines IL1b and TNFa. PLAURconnects to the tyrosine kinase LYN, the hub gene of the adaptive

immunity subnetwork, and to the integrin gene ITGB2 in the M2

macrophage subnetwork. It is also connected to the polymorphic

genes TNFSF10B and TNFAIP in the interferon network and to

TPM4, INHBA, THBS1, and CCL2 in the ECM subnetwork.

The centrality of PLAUR within the consensus gene network

suggests that PLAUR may be a key mediator of inflammatory and

ECM remodeling signals in SSc skin.

The proliferation subnetwork has predicted interactions with

the inflammatory and ECM subnetworks. The most pronounced

connection is between the ECM subnetwork and the cell

proliferation subnetwork through TGFb pathway genes (Fig. 6).

The TGFb pathway is known to modulate cell proliferation.

There are multiple paths from the TGFb pathway genes

TGFB3 and TGFBR2 to the cell proliferation subnetwork

through the polymorphic genes IRAK1 and PXK, which have

predicted interactions with the serine/threonine kinases LATS2,

WNK4, and PRKAA1. Serine/threonine kinases are well

known to be important regulators of cell proliferation and they

are bridges between the ECM subnetwork and cell proliferation

network.

Discussion

The intrinsic subsets of SSc have been found in multiple skin

gene expression datasets. Until now, the majority of experimental

data has indicated that the subsets are mutually exclusive—i.e.

patients are categorized as being in one of the subsets, and that the

core molecular processes and subsets of genes, are reproducible

across cohorts. Despite this consistency, the exact set of intrinsic

genes varies across datasets. We address both of these issues here.

Our consensus clustering approach allowed us to detect a

conserved set of genes from a module perspective across the three

independent SSc patient cohorts by considering molecular

processes first and constituent genes second. The predicted co-

expression interactions between these consensus genes indicate

that the key processes represented by the consensus genes

(inflammation, ECM remodeling, and cell proliferation) may

interact at a molecular level, with specific links between the

subnetworks. Thus, we have demonstrated theoretical connectionsbetween the genes of the SSc intrinsic subsets that are difficult to

capture experimentally.

It is clear that in addition to its clinical heterogeneity, SSc is a

genetically complex disease. Many risk alleles for SSc have been

identified, but each has only a modest odds ratio and the complete

picture of SSc will likely develop from the interactions between

various risk factors. The network of consensus genes demonstrates

that a significant fraction of the genes with risk alleles for SSc have

probable interactions with the consensus genes that underlie the

intrinsic gene expression subsets. This implicates these polymor-

phic genes as interacting with genes differentially expressed in the

subsets. This simultaneously provides a picture of the key gene

expression abnormalities in the intrinsic subsets and the validated

genetic associations at a systems level.

These data and the resulting network were developed from a

detailed meta-analysis of SSc skin gene expression datasets using

MICC, a consensus clustering framework we developed. Our

method reports only consensus clusters that are conserved across

all input datasets and dispenses with non-conserved gene

expression. The concept of mutual information gives MICC a

theoretical foundation, but like any data mining algorithm, its

value is gauged by performance on real data.

The rationale for gene coexpression clustering algorithms like

WGCNA is that co-expression networks are inherently modular

and that co-expression hub genes are likely related to the

regulation of the modules. This has been borne out by several

studies in humans [55], mice [56], and even across species [57].

The genes identified by MICC are disproportionately more hub-

like than a random population of the same size (S1 Fig.).

Therefore, MICC does not identify spurious overlaps but rather

detects network-relevant overlaps that are enriched for key hub

genes. At the same time, the information graph used by MICC is

not simply a disconnected set of triangles, which would indicate a

one-to-one mapping of modules between datasets. Instead, the

modules in one dataset are broken into a small set of pieces that

are re-assorted to build the modules in another dataset. This is

likely due to variations in study design and protocols between the

datasets, but also the inherent heterogeneity of SSc, therapy

effects, and environmental exposures. The MICC method is

explicitly designed to handle this unavoidable variance by

broadening the definition of consensus cluster to allow for

imperfect conservation of gene coexpression. We also note that

MICC is completely general with respect to the data that are

clustered and which clustering algorithms are used. In principle,

gene expression from different tissues (e.g. blood and skin) or

different species (e.g. mouse and human) or data from multiple

experimental modalities (e.g. transcriptomics and proteomics) can

be compared using MICC. These types of data exist for multiple

tissues in SSc and multiple animal models of SSc. Follow-up

studies will integrate these to further elaborate the molecular

underpinnings of SSc.

The consensus clusters from MICC show both skin-specific

processes that represent basic biological processes in this tissue as

well as disease-specific processes. Nearly all (24 out of 26)

consensus clusters are enriched only for general cellular or

otherwise skin-specific biology: metabolism, cell turnover, kerati-

nocyte-specific gene expression, etc. We view these consensus

clusters as a useful positive control for the MICC method. Such

housekeeping processes are clearly biologically relevant and

MICC would be missing important structure in the data if these

were not found. By taking a ‘‘module first’’ approach, MICC is

able to identify consensus genes that are specifically clustered into

pathologically active modules (the subset-specific modules).

The MICC-derived consensus clusters are enriched forknown mediators of SSc pathology and genetic riskfactors

Two of the consensus clusters were SSc subset-specific (Fig. 3B).

These clusters contain the key gene expression abnormalities in

SSc that are conserved across all three cohorts. The consensus

clusters are enriched for inflammatory process Gene Ontology

terms, as well as TGFb signaling, PDGF signaling, and cell

proliferation (Table 3). Most (30 out of 41) of the genes with

replicated SSc-associated polymorphisms are predicted to interact

with genes in the consensus clusters; 28 out of 30 of these interact

in the immune (interferon, M2 macrophage, and adaptive



immunity) and TGFb/ECM subnetworks (Fig. 4). The inflamma-

tory-specific consensus cluster also contains the genes FBN1 and

AIF1. Previous work implicates FBN1 in SSc pathogenesis, as a

duplication of FBN1 causes fibrosis in the Tsk1 mouse [58] and a

point mutation in FBN1 causes the fibrotic phenotype in the Stiff

Skin Syndrome mouse [59]. Fibrillin-1 forms a matrix of elastic

microfibrils that provide a scaffold for elastins and collagens, and a

means for sequestering matricellular growth factors. Mouse

embryonic fibroblasts expressing the Tsk1 mutant FBN1 have

altered microfibril morphology that results in increased collagen

deposition [58]. While polymorphisms in FBN1 might cause

dosage effects that result in fibrosis in some models (e.g. Tsk1), it is

possible that chronic inflammation causes chronic high expression

of FBN1 to similar effect in humans. Rare polymorphisms in

FBN1 have been associated with SSc in some subpopulations [60–

62].

Similarly, AIF1 is implicated in SSc disease progression. A SNP

in AIF1 has been implicated in anticentromere antibody (ACA)

positive SSc [63]. Moreover, AIF-1 is interferon-inducible,

constitutively expressed in macrophages [64], and plays a role in

vasculogenesis and endothelial cell proliferation and migration

[65]. In the Sclerodermatous Graft-Versus-Host Disease

(sclGVHD) mouse model of SSc, AIF1 was found to be highly

expressed in skin [66] and to induce fibroblast and monocyte

chemotaxis [53]. AIF1 has many predicted interactions with

chemokine receptors CCR1 and CX3CR1, which are connected to

chemokines CX3CL1 (fractalkine) and CCL2 (MCP-1). The genes

CX3CL1 and CCL2 are M1 and M2 macrophage-related genes

respectively [67] and are chemotactic for monocytes, macrophag-

es, and T cells [68], suggesting enhanced recruitment of

inflammatory cells to this subnetwork. A recent study of a mouse

model of SSc demonstrated that both CCR2 and CX3CR1regulate skin fibrosis, further implicating these mediators in the

pathogenesis of SSc [69]. In addition, CCL2 has been shown to

induce M2 macrophage polarization [70], which may result in

persistent M2 activation. The repeated and conserved finding of

high AIF-1 levels in the inflammatory subset and its tight

connection to innate immune mediators of inflammation suggest

it may be involved in enhanced macrophage chemotaxis and

activation in SSc skin.

LYN, a hub of the adaptive immunity subnetwork, modulates B

cell activation and plays a role in self-tolerance. B cell signaling has

been implicated in SSc development and progression, as B cells

have been shown to play a role in both the development of

autoantibodies and cutaneous fibrosis in the Tight Skin 1 (Tsk1)

mouse model of SSc. Notably, LYN is overactive in response to

overexpression of CD19 in this model [71]. Thus, LYN may play a

role in the autoimmune component of SSc in human patients.

The molecular network shows putative connectionsbetween subnetworks

The consensus gene network (Figs. 4 and 6) also implicates

genes as bridges between the subnetworks. These notably include

the polymorphic genes PLAUR, IRAK1, PXK, and GRB10. In

addition, we find differentially expressed genes straddling the

subnetworks including RAC2 and LCP2. The interconnections

between the subnetworks present possible molecular paths through

which these processes interact.

The finding that most SSc-associated polymorphisms are

associated with immune system mediators suggests that the initial

events in SSc are likely to be immune-regulated and to involve

interferon activation (Fig. 7). The immune response in SSc likely

differs from a normal response because of predisposing genetic

variants in these and associated genes. This may lead to the

secondary recruitment of macrophages via RAS-RAC signaling

(Fig. 7).

We predict that the interferon network suppresses cell

proliferation, given the clear distinction between the inflammatory

and fibroproliferative subgroups. This inference is based on known

interferon biology and not on the network itself. In contrast, it is

possible that the ECM network stimulates cell proliferation

through the TGFb pathway and serine/threonine kinases IRAK1,

LATS2, WNK4, and PRKAA1. In this model, inflammatory gene

expression creates a balancing feedback loop that modulates

fibroproliferative gene expression (Fig. 7).

A major strength of the IMP network and its data integration

capabilities derives from its ability to provide a more detailed

picture of SSc development and progression compared with more

conventional approaches. For example, while all of the purple

nodes in Fig. 4 are highly expressed in the inflammatory group

across all data sets, the IMP network provides information

regarding gene-gene interactions in addition to expression data.

In this example, the IMP network indicates which subnetworks

correspond to discrete processes (interferon, M2 macrophages,

ECM, and adaptive immunity) and which interactions are

mediated through the network. Thus, we gain insight by

recognizing that the interferon component is distinct from the

M2 macrophage component, despite their co-expression and

known interdependence. The value of the IMP network is as much

in the connections that are not present as those that are.

What is the nature of the interactions between theintrinsic gene expression subsets?

Since the original publication of the intrinsic subsets, two

important questions have been central to their interpretation and

their clinical relevance: First, can a patient’s subset change over

the course of their disease? And second, can the subsets predict

therapeutic response?

Pendergrass et al. [11] demonstrated that a patient’s subset is

stable over time scales of 6 to 12 months. This means either that

patients never change subsets and the intrinsic subsets are

effectively distinct diseases, or that the subsets are long-lived states

of the same disease. Our analysis shows that the inflammatory and

fibroproliferative subsets share a molecular network containing

TGFb pathway genes and ECM component genes, suggesting that

inflammatory patients may transition to the fibroproliferative

subset, perhaps in response to successful immunosuppressive

therapy. Indeed, immunosuppressive therapy has not been widely

successful for treatment of SSc [72]. On the other hand,

fibroproliferative biopsies still have some activation of the

TGFb/ECM network despite the absence of the inflammatory

signature (Fig. 4). The connection of the subsets through the

TGFb/ECM subnetwork indicates that the fibroproliferative

subset shares a common pathway with the inflammatory subset

and that the fibroproliferative subset is tied to chronic TGFbactivation and ECM deposition. Thus, based on the molecular

network, it is possible that immunosuppressive therapy can move

patients to the fibroproliferative subset rather than restoring their

gene expression to that of healthy skin. Our data from an ongoing

MMF clinical trial and analysis of mouse models of SSc suggests

that gene expression changes precede clinical changes [4,66];

therefore gene expression could act as a readout for the

effectiveness of a drug. This idea should be rigorously tested in

clinical trials that carefully monitor gene expression in patient skin

biopsies.

The pathogenesis of SSc has been enigmatic, but a number of

genetic risk factors have been identified by genome-wide

association studies and candidate gene studies. Three of these



polymorphic genes, NOTCH4, IRF7, and GRB10, are in the

inflammatory consensus cluster, and hence are consistently

differentially expressed in the inflammatory subset (Fig. 4). This

suggests that these may be cis-acting alleles and demonstrates the

need for candidate gene studies to determine if differential

expression is genetically driven in a subset of patients. The IMP

functional network predicts that twenty-five of the remaining forty-

one polymorphic genes interact with genes from the inflammatory

consensus cluster (Fig. 4). Rather than being scattered evenly

across all of the subsets or unrelated to any of the consensus genes,

the risk alleles are overwhelmingly related to the inflammatory

subset. The genetic studies, however, did not stratify their patients

by intrinsic gene expression subset. The studies were carried out as

case versus control or case versus case, when stratified by

autoantibody status or other clinical outcomes. Risk alleles

associated with a particular gene expression subset have not been

reported. We reemphasize the fact that we found no consensus

clusters that were differentially regulated in all SSc vs. healthy

control biopsies.

These data support the hypothesis that the subsets are related to

disease progression and that SSc starts with immune activation,

perhaps in response to an environmental trigger [73,74]. The

SNPs associated with SSc would then likely be risk factors for an

aberrant immune response to this trigger. Should such a model be

correct, we are still left with the question of why we have different

subsets that generally show little or no correlation with disease

duration. The simplest explanation for this result is that patients

progress through the gene expression subsets at dramatically

different rates and that our measures of disease duration are

currently inadequate.

Another possibility is that any given patient transitions between

these intrinsic gene expression groups in a dynamic manner that

we do not observe using serial skin biopsies across 6–12 month

time interval. This would mean that cross-sectional studies of

patients would still capture all subsets while maintaining a weak

correlation to disease duration. We think this is unlikely because

serial biopsies are generally found in the same subset.

The final possibility is that the subset a patient stays in, and the

duration in which they remain, is dependent on many outside and

as yet poorly characterized factors. These could include environ-

mental stimuli that trigger an inflammatory response, or genetic

factors that determine the rate at which one progresses through

the mechanistic stages of SSc. It is possible that patients in each

intrinsic subset have a different set of predisposing genetic

polymorphisms or similar environmental triggers. This can only

be addressed if we can look for genetic risk factors in a cohort of

Fig. 7. Model of interactions among the components of the network. The molecular network of Fig. 4 is densely interconnected, implicatingmany possible interactions between the core molecular processes (interferon activation, M2 macrophage activation, adaptive immunity, ECMremodeling, and cell proliferation). Stepping back from the granular detail of single genes, we see a system of distinct parts through which SSc couldbe initiated and maintained. Among these are paths of particular interest. The interferon subnetwork and the M2 macrophage subnetwork areconnected by RAC2. The M2 macrophage subnetwork in turn is connected to the ECM subnetwork through paths through CD14 and THY1.Suggesting macrophages may influence or drive ECM abnormalities in skin. The interferon subnetwork and the ECM subnetwork are connectedthrough paths containing the pleiotropic and polymorphic gene PLAUR. The M2 macrophage subnetwork is connected to the adaptive immunitysubnetwork through several distinct sets of paths through the genes GRB10, LCP2, and CXCR4. The ECM subnetwork is connected to the cellproliferation cluster through TGFb pathway genes and paths containing the polymorphic genes IRAK1 and PXK, which suggests that ECM remodelingmodulates cell proliferation through the TGFb pathway. The interferon node may negatively regulate proliferation via the ERK/MAPK pathwayresulting in the general mutual exclusivity of the inflammatory and fibroproliferative subsets. Thus we see a set of interconnected, balancingfeedback loops that can enforce subset homeostasis, but also allow for patients to transition between the subsets, possibly in response to therapy.doi:10.1371/journal.pcbi.1004005.g007



patients stratified by gene expression subset for genetic risk alleles.

There may be genetic risk factors that cause a patient to ‘‘stall’’ at

particular point along the progression from inflammatory to

proliferative to normal-like. Genetic modifiers of the molecular

links in the consensus gene network (Fig. 4) might hold the key to

showing why many patients go into spontaneous remission while

others experience rapid clinical progression, and indeed, our

network analysis suggests candidates for explaining this (Figs. 6, 7).

For example, IRAK1 and PXK are polymorphic genes that exist

on paths in the network between the TGFb/ECM network and

the cell proliferation network. This strongly argues for future

studies that test their possible roles in TGFb-modulated cell

proliferation, with particular attention to their roles in influencing

other serine-threonine kinases that modulate the cell cycle.

Associations with autoantibodiesThe presence of antinuclear autoantibodies in patient serum is a

widely used biomarker of SSc. To date the intrinsic subsets have

shown no clear association with autoantibody status [1,4,11],

which is consistent with a model by which the subsets represent

disease progression. Several genetic polymorphisms are associated

with autoantibody status (S9 Data file), including BLK and

BANK1, which are related to ACA- and ATA-positive SSc

respectively. These B cell proteins are already attractive candidates

for autoantibody production, as they are directly associated with

the cells that produce the antibodies, but our network analysis also

shows that they are functionally related to adaptive immune genes

that are highly expressed in the inflammatory subset.

Conclusions and limitationsA primary role of bioinformatics in complex diseases is to pare

down the possibilities to a coherent set of candidates for future

study. The many risk alleles for SSc each have modest odds ratio

and the final picture of SSc will likely lie in the interactionsbetween various risk factors, but the number of possible

interactions between these combinatorial factors is prohibitively

large. It is here that the network approach may be most useful in

delineating candidates for interaction studies. We might speculate,

for example, that SSc results from the presence of multiple,

functionally distinct alleles, but that it does not matter what gene is

mutated as long as the mutation has a particular functional

outcome. The predicted interactions in the network suggest which

alleles might be functionally related and which might be distinct

from each other, as the alleles either cluster within a subnetwork or

straddle the subnetworks.

This report is limited by our utilization of whole skin biopsies,

which are complex mixtures of cells, and in that the studies were

observational. The use of whole skin means that we cannot directly

ascribe gene expression to specific cell types. For example, we inferthat the M2 macrophage subnetwork is related to that cell type

based on the coherent expression of monocyte markers and

cytokines related to M2 polarization of macrophages. Our study is

therefore hypothesis generating. Mechanistic studies will be needed

to evaluate the existence of the molecular links suggested by the

network analysis.

Nevertheless, our analyses place the intrinsic subsets as a

possible readout of SSc pathology. The consensus gene expression

of the subsets implicates a number of molecular mechanisms that

have been associated with SSc and suggests functional roles for a

large fraction of the replicated SSc-associated polymorphisms. We

demonstrate that the core molecular processes of the inflammatory

and fibroproliferative subsets are molecularly connected to each

other. This suggests the possibility that SSc subsets may be

dynamic and interconnected.

Materials and Methods

Ethics statementThe analysis of prospectively collected human samples in this

study was approved by the Committee for the Protection of

Human Subjects at Dartmouth College (CPHS#16631) and by

the IRB review panel at Northwestern Feinberg School of

Medicine (STU00004428). All subjects in the study provided

written consent, which was approved by the IRB review panels at

Dartmouth College and Northwestern Feinberg School of

Medicine.

Patient informationThis study used data from three previously published cohorts

(Table 1). Each of the studies is available from NCBI GEO at the

following accession numbers: Milano et al. (GSE9285), Pender-

grass et al. (GSE32413) and Hinchcliff et al. (GSE45485). We used

an expanded version of the Hinchcliff dataset that contained an

additional 12 SSc patients, 1 healthy control and 1 morphea

patient beyond what was included in Hinchcliff et al.[4]

(GSE59785).

Each of the three study cohorts contained patients with SSc

defined using the 1980 ACR criteria. Specifically, all patients met

the American College of Rheumatology classification criteria for

SSc [75] and were further characterized as the diffuse (dSSc), or

the limited (lSSc) subsets. Limited SSc patients had 3 of the 5

features of CREST syndrome, or had Raynaud’s phenomenon

with abnormal nail fold capillaries and scleroderma-specific

autoantibodies.

Preprocessing and clustering of microarray dataAll three studies used Agilent Technologies 44,000 element

DNA microarrays representing the full human genome. All

samples were processed and all microarrays hybridized in the

Whitfield lab providing consistency between the datasets. The

DNA probes between these datasets are identical and thus were

indexed using the same probe identifiers allowing direct mapping

from one data set to another without significant loss of data.

Microarray data from each cohort were Log2 Lowess-normalized

and only spots with mean fluorescent signal at least 1.5 greater

than median local background in Cy3- or Cy5- channels were

included in the analysis. Genes with less than 80% good data were

excluded. Since a common reference experimental design was

used for all cohorts, each probe was centered on its median value

across all arrays. Data were multiplied by -1 to convert them to

Log2(Cy3/Cy5) ratios.

The three cohorts were clustered into coexpression modules

using the WGCNA procedure. We used the WGCNA R package

available on the Comprehensive R Archive Network (http://cran.

r-project.org) and described in [10]. We used the default

parameters for running the software except that we used the

‘‘signed’’ network option and a soft thresholding parameter d = 12.

These parameters are described in depth in [10,15]. Genes that

were classified as outliers were discarded from further analysis.

The information graph and consensus clustersTo each pair of modules from different datasets we associate an

overlap score W. Specifically, if Ci is a module in, say, Milano et

al. and Cj a module in Pendergrass et al., then we define

Wij~DCi

TCj D

Nlog

DCi

TCj DN

DCi DDCj D



http://cran.r-project.org

http://cran.r-project.org

where N is the total number of genes in the genome. The W-scores

can be interpreted as edge weights in a module-module network

(the information graph). This network encodes the mutual

information between the WGCNA-derived genomic partitions.

We computed the W-scores between each pair of modules across

all three datasets by the above formula and set the small and

negative W-scores below a threshold to zero (S4 Fig.). A

mathematical derivation of the relationship between the W-scores

and mutual information and a detailed description of the

thresholding procedure are available in the supporting information

(S1 Text). The resulting 3-partite information graph was mined for

consensus clusters.

Since triangles in the information graph represent a module

conserved across all three datasets, we clustered the information

graph using a variant of triangle percolation [17], which is a

community detection procedure designed to find sets of modules

that are members of many triangles together. Specifically, from the

information graph we constructed an auxiliary graph, called the

triangle graph, and detected communities in the triangle graph by

greedy modularity maximization [76]. A description of the

construction of the triangle graph is available in the supporting

information (S1 Text).

We define a final consensus cluster as all of the genes that are

contained in a module from the community for each of the three

data sets community. Note that triangle percolation allows for

overlapping communities in the underlying information graph.

For example, the inflammatory consensus cluster and the

keratinocyte consensus cluster overlap by one module (Fig. 3A).

This is one of MICC’s strengths because it does not require a

whole module from one dataset to be associated with only one

consensus cluster. To derive a gene set associated to the consensus

cluster, we took all modules within that community, computed

their unions within their dataset, and then computed their

intersection across datasets. In symbols, let Comm denote a set of

modules that form a community in the information graph (e.g. the

dotted circles Fig. 1B and the colored nodes of Fig. 3A). Let

MComm, PComm, and HComm denote respectively the sets of Milano,

Pendergrass and Hinchcliff modules within Comm. Let m, p, and hdenote modules in the Milano, Pendergrass, and Hinchcliff data

sets respectively; note that these are sets of genes. We associate a

gene set CCComm with the community Comm through the

following formula:

CCComm~[

m[MComm

m

0@

1A\ [

p[PComm

p

0@

1A\ [

h[HComm

h

0@

1A

We call CCComm the consensus cluster associated with thecommunity Comm and it consists of all genes that are present in

a module from each data set within the community. The elements

of CCComm are the consensus genes. It is clear by definition that the

consensus clusters are nonoverlapping even though communities

can share modules. This is because a gene needs to be present in a

module in the community from each of the three data sets. Since

modules do not overlap within data sets, consensus clusters cannot

either.

Statistical tests for subset specificityTo determine if a WGCNA-derived module was significantly

differentially regulated in a subset, we performed one-tailed

Wilcoxon rank sum tests. Specifically, we computed the module

eigengene of each module by first normalizing the gene expression

so that each gene expression vector had Euclidean length 1. The

module eigengene is the first principal component of the

normalized gene expression vectors within the WGCNA module.

The module eigengene is a one-dimensional summary score for

the module’s gene expression across all biopsies. To determine if

the module was significantly up- or down-regulated in a particular

subset, we determined if the median of the module eigengene for

that subset was above or below that of the whole population, and

then performed a one-tailed Wilcoxon rank sum test to determine

the significance of the median being above or below that of the

population as a whole. We used the subset assignments reported in

the previous papers describing these datasets [1,4,11]. We used

Bonferroni corrections for multiple comparisons. There were 178

modules in total across the datasets. In Table 2, we corrected for

17863 tests for each of the subset-specificity tests. In Fig. 3B, we

corrected for 17864 tests because we included tests for all non-

normal-like SSc versus normal-like SSc and healthy controls (see

also S4 Data file).

The IMP Bayesian networkThe IMP Bayesian network is available through an online

interface at (http://imp.princeton.edu). To build our network, we

queried IMP with four gene sets: inflammatory and fibroproli-

ferative consensus genes derived from the consensus clusters, SSc-

associated polymorphisms (as described below), and the four gene

MRSS biomarker reported in [5]. IMP provides export of the

subnetwork corresponding to the query genes as a weighted edge

list (a three-column table indicating which genes are connected

and with what probability). IMP automatically thresholds the

probabilities at 0.5 and exports the network with up to an

additional 50 genes that provide extra context for the query genes.

In our case, the 50 genes were predominantly cell cycle genes. This

is probably because the cell cycle is heavily studied in the

microarray compendium from which IMP was built. In that case,

IMP would be highly confident about predicting interactions

between the fibroproliferative genes and other cell cycle genes.

We developed in-house Matlab and R scripts to transform the

edge list data into the Graph Exchange Format (gexf), which

allows for manipulation in Gephi, an open source network

visualization program [77]. Data files S7-S8 contain post-

processed networks and Data files S10-S11 provide R data and

code snippets for manipulating the network programmatically.

Genetic polymorphisms for network analysisWe collected genes with SSc-associated polymorphisms from

the literature and curated them according to the following criteria.

We included polymorphic genes that were reported in genome-

wide association studies of SSc [78–82], from a recent study using

the Immunochip platform [83] and from case-control candidate

gene studies that were replicated in at least one other study

[54,84–105]. This resulted in a list of 41 polymorphic genes (S6

Data file).

Supporting Information

S1 Fig Consensus genes are enriched for coexpression hubs. The

consensus genes reported by MICC are more correlated to their

module eigengene (red density) than is typical for an arbitrary

gene-eigengene correlation (blue density). Genes are compared

only to their module eigengene, i.e. to the hub that the gene is

closest to in the coexpression network. Note the evident shift in the

red density toward 1, which is perfect correlation, indicating that

consensus genes are more ‘‘hub-like’’.

(TIF)

S2 Fig Adjacency matrix for the triangle graph. The triangle

graph is a weighted graph whose nodes are triangles in the



http://imp.princeton.edu

information graph and whose edges indicate that the correspond-

ing triangles in the information graph share and edge. (A) The

weighted adjacency matrix for the triangle graph. Rows and

columns of the adjacency matrix are indexed by nodes of the

triangle graph (i.e. by triangles in the information graph). The

rows and columns of the matrix are sorted according to

community order. Note the distinct block structure of the

matrix indicating that the underlying graph is highly modular.

(B) The same matrix, but unweighted so that the matrix

contains only 0’s and 1’s (blue and red cells in the matrix,

respectively) indicating that the nodes are either connected or

disconnected. This aids in the visualization of the community

structure of the graph (block structure of the matrix), although

community detection was performed on the weighted triangle

graph.

(TIF)

S3 Fig Schematic for building consensus gene sets. To each

community (1) in the information graph we associate a consensus

gene set by (2) computing the union of modules within a data set

and then (3) computing the intersection across data sets.

(TIF)

S4 Fig Construction of the information graph. (A) Three pairs of

partitions of a 12-element set and their associated bipartite

information graphs. Edge width denotes the size of the W-score for

a pair of modules. Dotted edges represent negative W-scores. The

highest possible mutual information occurs when modules are

perfectly conserved. The information graph is disconnected with

edges denoting the mapping between conserved modules. In the

intermediate case, modules break into pieces that are reassorted

among each other. The information graph here has strong

community structure, but is not completely disconnected. The low

mutual information case occurs when the partitions labels are

random with respect to each other. In this case, all edges are small

and are partially cancelled by the negative edges also present in the

graph. (B,C) W-scores are calculated for each pair of modules; in

this case one from Milano and one from Pendergrass. (B) Most W-

scores are small in absolute value (blue histogram; logarithm of

density), while their distribution has a right tail of significantly

large scores. We can threshold the small and negative W-scores by

keeping only those scores that contribute positively to the total

mutual information (red curve; x-intercept). The sum of all W-

scores is the total mutual information between the Milano and

Pendergrass genomic partitions (dashed blue horizontal line). (C)

The W-scores are positively correlated with the size of the overlap

between gene clusters, but the relationship is not perfect. The W-

score threshold is shown by a dotted blue vertical line and the

overlaps that exceed the threshold are plotted in red. In particular,

note that there are relatively large overlaps that fail to meet the

threshold. Likewise, there are relatively small overlaps that have

high W-scores.

(TIF)

S1 Text Additional mathematical details about the MICC

method and glossary of keywords used in main text.

(PDF)

S1 Data file WGCNA clustered PCL file for Milano skin data.

(ZIP)

S2 Data file WGCNA clustered PCL file for Pendergrass skin

data.

(ZIP)

S3 Data file WGCNA clustered PCL file for Hinchcliff skin

data.

(ZIP)

S4 Data file Table of p-values for modules in each dataset

(includes module sizes).

(XLSX)

S5 Data file Full output of g:Profiler for consensus clusters.

(XLS)

S6 Data file Complete list of polymorphic genes used in this

study.

(XLSX)

S7 Data file Molecular network plotting file GEXF format.

(GEXF)

S8 Data file Molecular network plotting file Gephi format.

(ZIP)

S9 Data file Molecular network plotted in PDF (text searchable

for genes).

(PDF)

S10 Data file R data for programmatic access to network

(iGraph format).

(ZIP)

S11 Data file R code snippet demonstrating reading and writing

graphs from R to GEXF format.

(R)

Author Contributions

Conceived and designed the experiments: JMM MLW. Performed the

experiments: TAW MEH. Analyzed the data: JMM JT VM TAW CSG.

Contributed reagents/materials/analysis tools: MEH. Wrote the paper:

JMM PAP MEH MLW.

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systems level analysis of systemic sclerosis shows a ......systemic sclerosis (ssc) is a rare...

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